Popular Computational Tools Used for miRNA Prediction and Their Future Development Prospects
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REVIEW
Popular Computational Tools Used for miRNA Prediction and Their Future Development Prospects Tianyang Yu1,2 · Na Xu1,2 · Neshatul Haque3 · Chang Gao1 · Wenhua Huang1,4 · Zunnan Huang1,2,5 Received: 7 April 2020 / Revised: 13 August 2020 / Accepted: 19 August 2020 © International Association of Scientists in the Interdisciplinary Areas 2020
Abstract MicroRNAs (miRNAs) are 19–24 nucleotide (nt)-long noncoding, single-stranded RNA molecules that play significant roles in regulating the gene expression, growth, and development of plants and animals. From the year that miRNAs were first discovered until the beginning of the twenty-first century, researchers used experimental methods such as cloning and sequencing to identify new miRNAs and their roles in the posttranscriptional regulation of protein synthesis. Later, in the early 2000s, informatics approaches to the discovery of new miRNAs began to be implemented. With increasing knowledge about miRNA, more efficient algorithms have been developed for computational miRNA prediction. The miRNA research community, hoping for greater coverage and faster results, has shifted from cumbersome and expensive traditional experimental approaches to computational approaches. These computational methods started with homology-based comparisons of known miRNAs with orthologs in the genomes of other species; this method could identify a known miRNA in new species. Second-generation sequencing and next-generation sequencing of mRNA at different developmental stages and in specific tissues, in combination with a better search and alignment algorithm, have accelerated the process of predicting novel miRNAs in a particular species. Using the accumulated annotated miRNA sequence information, researchers have been able to design ab initio algorithms for miRNA prediction independent of genome sequence knowledge. Here, the methods recently used for miRNA computational prediction are summarized and classified into the following four categories: homologybased, target-based, scoring-based, and machine-learning-based approaches. Finally, the future developmental directions of miRNA prediction methods are discussed.
Tianyang Yu, Na Xu and Neshatul Haque have contributed equally to this work. * Wenhua Huang [email protected] * Zunnan Huang [email protected] 1
2
Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan, Guangdong 523808, China Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong 523808, China
3
Structural Biology Laboratory, Division of Applied Biology, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India
4
National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, No. 1838 Guangzhou Road North, Guangzhou, Guangdong 510515, China
5
Marine B
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